Modelling of memristive learning behaviour and mnemonic similarity tasks

• Applicants: Professor Dr. Thorsten Bartsch; Professor Dr. Christian Kaernbach; Professor Dr. Martin Ziegler
• Subject Area: Cognitive, Systems and Behavioural Neurobiology ; Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering ; Human Cognitive and Systems Neuroscience
• Term: from 2014 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 239767484

The aim of the first funding period was to establish a model of cellular and network-dependent mechanisms of hippocampal learning within memristive circuit models. We investigated the emulation of key cognitive functionalities of the hippocampus to memristive systems. Based on the findings of the first funding period, the project aims at extending its conceptual and methodological framework to model cellular and network-dependent forms of hippocampal learning and neurobiologically-inspired mechanisms within memristive circuit models. The methodological extension is based on the (i) translation of new human behavioural models into the memristive hippocampal circuit model (ii) the study of the dynamics of network interaction and (iii) provide the conjunction to emulate those concepts with 'real' memristive devices. The conceptual extension aims at further characterizing (i) relevant functional states of this model (global network state changes and network perturbation) and (ii) introducing critical functional extensions (spatial pattern separation) of hippocampal function including its (iii) translation towards cognitive engineering. A close connection of the hippocampal network model to neuroplasticity data should ensure neurobiological relevance. This parallel and translational approach of modelling relevant biological mechanisms with memristor-based networks should allow bridging the gap between neurobiology and cognitive electronics.

• DFG Programme: Research Units
• Subproject of: FOR 2093: Memristive Devices for Neural Systems